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Multiagent Systems & Orchestration

AI Agents Architecture

Portfolio of agentic workflow patterns

Project Init sys_vars.json
Context
SYS>AI Agents Architecture shows how multiagent orchestration becomes a working production system with clear responsibilities, constraints, and evidence.
Role
USR>Lead AI Architect / Systems Engineer
Problem
ERR>The core problem is avoiding one-off AI output and keeping the workflow traceable, reviewable and production-oriented.

Constraints

  • ■ Separate the control plane that routes and approves work from the runtime that executes tools.
  • ■ Build deterministic context assembly so agents receive memory, files, and task state intentionally.
  • ■ Use scoring, validators, and human approval gates before outputs become production changes.
Architectural Themes

Foundation of agentic systems

Core principles that separate full autonomous systems from simple LLM integrations.

Integration pattern

Agents are embedded into business workflows with roles, contracts, and success criteria, not exposed as generic chatbots.

Architecture Control Plane

The control plane contains state, routing, authority, memory, gates, rollback, and verification rules.

Context & Memory Layer

MemoryOS, context packs, and retrieval decide what reaches the prompt so the agent sees the relevant project slice.

Deterministic Validation

Scoring, AST checks, Pydantic validators, and human approval provide proof outside the model response.

Cost & Model Routing

Model fallback, cascade routing, and context pruning route work according to risk, cost, and complexity.

Agent pattern

Local-first adapters, SQLite, markdown, and deterministic interfaces keep prototypes practical and inspectable.

Production Systems

6 architectural systems

A molecular breakdown of real projects. For each system, the workflow sequence, inputs, tools, and QA gates are documented.

Desktop AI-First ERP

GovardOS / ERP2

Architecture

Workspace-native orchestration with a strict control plane separated from local execution runtime.

Agents

OpenAgent orchestrator, OpenCoder implementation worker, and System Builder architecture agent.

Pipeline Sequence
Intent capture from the user Intent capture from the user
→
Route to the right specialist subagent Route to the right specialist subagent
→
Load context from local MemoryOS and files Load context from local MemoryOS and files
→
Execute through approved tools Execute through approved tools
→
Ask for approval on risky actions Ask for approval on risky actions
→
Commit outputs and save memory Commit outputs and save memory
Inputs
Filesystem data Context packs (DCP)
Tools
MCP servers for terminal, filesystem, memory, Context7, and browser automation
Validation Gates
Human-in-the-loop approval ExecutionLock Automated smoke tests Typecheck
01
Agentic Harness & PMO

OpenCode Setup / Factory OS

Architecture

Planner-worker separation with subagent spawning and policy-gated tool access.

Agents

Router, research, decomposition, coder, tester, reviewer, docwriter, and workflow-specific specialists.

Pipeline Sequence
Understand: research agent scans the knowledge base Understand: research agent scans the knowledge base
→
Plan: decomposition agent turns scope into steps Plan: decomposition agent turns scope into steps
→
Implement: coder modifies files Implement: coder modifies files
→
Test: tester writes or runs checks Test: tester writes or runs checks
→
Verify: reviewer performs read-only risk review Verify: reviewer performs read-only risk review
Inputs
Markdown command assets Current project code and docs
Tools
Grep Glob AST parsing Sandboxed bash
Validation Gates
GateLedger guardrails Architecture validators G3/G4 gate verdict before commit
02
Artifact-Driven Analysis

Website Engine Pipeline

Architecture

Artifact-driven analysis pipeline with discrete phases; no one-prompt website generation.

Agents

Strategy, design, implementation, marketing, review, and QA roles chained through route artifacts.

Pipeline Sequence
/brief defines niche and style /brief defines niche and style
→
Prompt assembly builds XML from fragments Prompt assembly builds XML from fragments
→
Generation produces raw HTML Generation produces raw HTML
→
/polish improves design /polish improves design
→
/marketing strengthens copy /marketing strengthens copy
→
/review checks responsiveness /review checks responsiveness
→
/score applies final quality gate /score applies final quality gate
Inputs
Niche and segment Style tokens XML fragments
Tools
Puppeteer Lighthouse metrics
Validation Gates
Seven-metric scoring system Total score ≥ 82 Output verification loop
03
Config-Driven RAG

AI Page Generator v2

Architecture

Agentic search over vector embeddings with failover-aware model fallback.

Agents

Multiagent router, RAG retrieval engine, code/layout generator, and validation loop.

Pipeline Sequence
Init receives JSON page config Init receives JSON page config
→
Router estimates complexity Router estimates complexity
→
RAG search filters relevant snippets RAG search filters relevant snippets
→
Mass generation creates HTML/HBS/JSON combinations Mass generation creates HTML/HBS/JSON combinations
→
Assembler builds page blocks Assembler builds page blocks
→
Fix loop adjusts contrast and UI hierarchy Fix loop adjusts contrast and UI hierarchy
Inputs
JSON configs for industry and style RAG hits with HTML/CSS snippets
Tools
Vector databases (Chroma, Qdrant) Directus API client AST parsers (BeautifulSoup)
Validation Gates
Contrast fixer Visual hierarchy validator Schema validation retry
04
StateGraph & Routing

LangGraph Product Consultant

Architecture

Explicit StateGraph with nodes, conditional routing, persistence, and fallback handoff.

Agents

Classifier, retriever, answerer, fallback/handoff node, and persistence adapter.

Pipeline Sequence
Ingest user question Ingest user question
→
Classify intent Classify intent
→
Retrieve local catalog context Retrieve local catalog context
→
Check confidence Check confidence
→
Answer or hand off to a human Answer or hand off to a human
Inputs
User messages Seed product and FAQ data
Tools
Local SQLite retrieval SQLAlchemy persistence Deterministic local adapters
Validation Gates
Pydantic validation No-hallucination handoff gate for low confidence
05
Atomic Design Engine

HTML-Tailwind Builder

Architecture

Static service manifest for agents and an LLM-friendly component API.

Agents

JSON-driven variation builders and preview validators.

Pipeline Sequence
Select components through Atomic Design levels Select components through Atomic Design levels
→
Load JSON schemas and Handlebars templates Load JSON schemas and Handlebars templates
→
Render and create UI variations through VariationEngine Render and create UI variations through VariationEngine
Inputs
Component registry Atomic design system JSON schemas
Tools
Preview and validation server Handlebars engine
Validation Gates
ConfigManager validates structures with hot reload
06
Project Conclusion

What this proves

Agent-Native Architecture

Uses deterministic validators to keep multiagent systems safe, inspectable, and repeatable.

Tool contract

Demonstrates how the same orchestration pattern supports web production, CRM, RAG, and UI generation.

Layered Governance

Turns agent architecture into a portfolio of practical systems rather than a set of isolated demos.

Deterministic Validation

Defines agent roles, tool boundaries, memory inputs, and review gates before execution begins.

Use the same approach for production systems that need AI acceleration without losing control.

Related materials

Website Engine Architecture
GovardOS Concept

Want agentic systems that are useful beyond a demo?

I design agent workflows around context, tools, memory, routing, approvals, and evidence so they can support real product and engineering work.

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